Philosophical Transactions of the Royal Society A, 366, 2561-2579, 10.1098/rsta.2008.0033
Impact of a quasi-stochastic cellular automaton backscatter scheme on the systematic error and seasonal prediction skill of a global climate model
Judith Berner, Francisco J. Doblas-Reyes, Tim N. Palmer, Glenn Shutts and Antje Weisheimer
ECMWF, Shinfield Park
RG2 9AX, Reading, UK
The impact of a nonlinear-dynamic cellular automaton model, as a representation of the
partially-stochastic aspects of unresolved scales in global climate models, is studied
in the European Centre for Medium-Range Weather Forecasts coupled ocean-atmosphere model.
Two separate aspects are discussed: impact on the systematic error of the model, and
impact on the skill of seasonal forecasts. Significant reductions of systematic error are
found both in the tropics and in the extratropics. Such reductions can be understood in
terms of the inherently nonlinear nature of climate, in particular how energy injected by
the cellular automaton at the near gridscale can backscatter nonlinearly to larger scales.
In addition, significant improvements in the probabilistic skill of seasonal forecasts are
found in terms of a number of different variables such as temperature, precipitation and
sea-level pressure. Such increases in skill can be understood both in terms of the reduction
of systematic error as mentioned above, and in terms of the impact on ensemble spread of
the cellular automaton's quasi-stochastic representation of inherent model uncertainty.